3D Food Printing Applications Related to Dysphagia: A Narrative Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Dysphagia is a condition in which the swallowing mechanism is impaired. It is most often a result of a stroke. Dysphagia has serious consequences, including choking and aspiration pneumonia, which can both be fatal. The population that is most affected by it is the elderly. Texture-modified diets are part of the treatment plan for dysphagia. This bland, restrictive diet often contributes to malnutrition in patients with dysphagia. Both energy and protein intake are of concern, which is especially worrying, as it affects the elderly. Making texture-modified diets more appealing is one method to increase food intake. As a recent technology, 3D food printing has great potential to increase the appeal of textured foods. With extrusion-based printing, both protein and vegetable products have already been 3D printed that fit into the texture categories provided by the International Dysphagia Diet Standardization Initiative. Another exciting advancement is 4D food printing which could make foods even more appealing by incorporating color change and aroma release following a stimulus. The ultra-processed nature of 3D-printed foods is of nutritional concern since this affects the digestion of the food and negatively affects the gut microbiome. There are mitigating strategies to this issue, including the addition of hydrocolloids that increase stomach content viscosity and the addition of probiotics. Therefore, 3D food printing is an improved method for the production of texture-modified diets that should be further explored.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it